class_docstr: An object that slices tensors into rows or columns as described in the Megatron LM paper
  
  Args:
      mp_group (torch.distributed.ProcessGroupNCCL): Distributed group for model parallelism
docstr: Slice weight and bias for model parallelization
  
  Args:
      policy_inputs (Tuple): tuple of weight and bias dictionaries
      attributes (Tuple): tuple of weight attributes and bias attributes dictionaries
      dim (int): dimension for slicing
      slice_bias (bool): whether slice bias or not
  
  Returns:
      Tuple: tuple of weights and biases
docstr_tok: ["Slice","weight","and","bias","for","model","parallelization","Args",":","policy_inputs","(","Tuple",")",":","tuple","of","weight","and","bias","dictionaries","attributes","(","Tuple",")",":","tuple","of","weight","attributes","and","bias","attributes","dictionaries","dim","(","int",")",":","dimension","for","slicing","slice_bias","(","bool",")",":","whether","slice","bias","or","not","Returns",":","Tuple",":","tuple","of","weights","and","biases"]
code: def slice_weight_and_bias(
      self,
      policy_inputs: Tuple,
      attributes: Tuple,
      dim: int,
      slice_bias: bool,
  ) -> Tuple:
      """
      Slice weight and bias for model parallelization
  
      Args:
          policy_inputs (Tuple): tuple of weight and bias dictionaries
          attributes (Tuple): tuple of weight attributes and bias attributes dictionaries
          dim (int): dimension for slicing
          slice_bias (bool): whether slice bias or not
  
      Returns:
          Tuple: tuple of weights and biases
      """
      weight, bias = policy_inputs
      w_attr, b_attr = attributes
      weight = self.slice_tensor(
          weight,
          w_attr,
          dim,
          is_bias=False,
      )
  
      if slice_bias:
          bias = self.slice_tensor(
              bias,
              b_attr,
              0,
              is_bias=True,
          )
      else:
          bias = tuple(bias.values())
  
      return weight, bias
  
file_path: parallelformers/parallelformers/parallel/slicing.py
imports: import os
  from typing import Dict, Tuple
  import torch
  import torch.distributed
label_desc: Use image node_id 3 for calling the TensorSlicer obj's underlying member method code with example usage: obj.slice_weight_and_bias(policy_inputs, attributes, dim, slice_bias) and returns: weight,  bias